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Formosan Sambar Deer Space Use and Multiscale Habitat Selection Using Habitat Suitability Modelling and GPS Telemetry

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Formosan Sambar Deer Space Use and Multiscale Habitat Selection Using Habitat Suitability Modelling and GPS Telemetry 國立臺灣師範大學生命科學系博士論文 以棲地適合度模式與 GPS 遙測技術探討 臺灣水鹿之空間使用及不同尺度下之棲 地選擇方式 Formosan sambar deer space use and multiscale habitat selection using habitat suitability modelling and GPS telemetry 研 究 生：顏士清 Shih-Ching Yen 指導教授：王穎 博士 Ying Wang 中華民國 102 年 7 月 Table of Contents Pages 摘要 1 Abstract 3 Introduction 5 Methods 10 Results 23 Discussion 27 Management implications 38 Conclusion 40 References 41 List of Tables Pages Table 1 The elevational distribution of Formosan sambar deer (Rusa 55 unicolor swinhoii) presence-absence records in Taiwan. Table 2 List of environmental variables used to predict the distribution of 56 Formosan sambar deer (Rusa unicolor swinhoii) in Taiwan. Table 3 Ranks of gain contributions of environmental variables in the 4 57 habitat suitability models: logistic regression, discriminant analysis, Ecological-Niche Factor Analysis (ENFA), and Maximum Entropy (Maxent). The other habitat suitability model, Genetic Algorithm for Rule-set Production, could not be used to compare the gain contributions of each variable. ENFA could not be used to compute nominal variables; therefore, the variable “vegetation type” was excluded from ENFA computation. Table 4 Data describing locations collected from Formosan sambar deer 58 with GPS collars in Taroko National Park, Taiwan, from December 2009 to July 2013. Table 5 Properties of the habitat types used to analyze habitat selection by 59 Formosan sambar deer in Taroko National Park, Taiwan, from December 2009 to July 2013. Table 6 Second-order habitat selection (home-range scale) by individual 60 deer (n=12) by using landscape attributes in a Formosan sambar deer GPS telemetry study in Taroko National Park, Taiwan, from December 2009 to July 2013. “Number of deer ” reflects the number of individual deer with mean values greater than (+), less than (-), or equal to (0) the mean values of study area, based on significant t-test (for slope, elevation, and solar duration) (P < 0.05) and Bonferroni confidence interval (for aspect) between home range and study area. Table 7 Second-order habitat selection (home-range scale) of habitat type 61 determined by Euclidean distance analysis using location data by 12 Formosan sambar deer in Taroko National Park, Taiwan, from December 2009 to July 2013. Table 8 Third-order habitat selection (within-home-range sacle) by 62 individual deer (n=12) by using landscape attributes in a Formosan sambar deer GPS telemetry study in Taroko National Park, Taiwan, from December 2009 to July 2013. “Number of deer ” reflects the number of individual deer with mean use values greater than (+), less than (-), or equal to (0) the mean value of an associated set of random locations, based on significant t-test (for slope, elevation, and solar duration) (P < 0.05) and Bonferroni confidence interval (for aspect) between use and random locations. Table 9 Third-order habitat selection (within-home-range scale) of habitat 63 type determined by Euclidean distance analysis using location data by 12 Formosan sambar deer in Taroko National Park, Taiwan, from December 2009 to July 2013. Table 10 Comparison of the distances (m) from forest pathes between 64 daytime and night locations by 12 collared Formosan sambar deer in Taroko National Park. Table 11 Summary statistics for 100% minimum convex polygon (MCP) 65 and 95% fixed-kernel home ranges (ha) of Formosan sambar deer estimated by GPS telemetry in Taroko National Park, Taiwan, from December 2009 to July 2013. Table 12 Summary statistics for daily displacement of Formosan sambar 66 deer estimated by GPS telemetry in Taroko National Park, Taiwan, from December 2009 to July 2013. List of Figures Pages Figure 1 (a) Elevation map of Taiwan, showing the locations of the 67 mountain ranges; (b) Distribution of protected areas across Taiwan. Figure 2 Predicted habitat of Formosan sambar deer (Rusa unicolor 68 swinhoii) in Taiwan by using logistic regression, discriminant analysis, Ecological-Niche Factor Analysis (ENFA), Genetic Algorithm for Rule-set Production (GARP), and Maximum Entropy (Maxent). Figure 3 (a) Predicted habitat of Formosan sambar deer (Rusa unicolor 69 swinhoii) in Taiwan by using ensemble forecasting. There are 3 highways crossing the main habitats that are suitable for sambar deer: Central Cross-Island Highway, Southern Cross-Island Highway, and Highway No. 7A; (b) Recorded locations of Formosan sambar deer (Rusa unicolor swinhoii) in Taiwan. Data were collected from field surveys (2008–2011) and assimilated previous studies (2002–2007). Figure 4 Map of the study area showing the habitat types in the habitat 70 selection analysis. The study area was 100% minimum convex polygon generated by all locations from 12 Formosan sambar deer tracked between December 2009 and July 2013. Figure 5 The mean and range of slopes used by collared Formosan sambar 71 deer in Taroko National Park, Taiwan, from December 2009 to July 2013. Figure 6 Mean elevations of the collared Formosan sambar deer locations 72 during cold/dry season (November to April) and hot/wet season (May to October) in Taroko National Park, Taiwan, from December 2009 to July 2013, shown as mean ± SD. Figure 7 The proportions of aspects used by 12 Formosan sambar deer 73 during cold/dry season (November to April) and hot/wet season (May to October) in Taroko National Park, Taiwan, from December 2009 to July 2013, shown as mean ± SD. The aspects were reclassified into 3 moisture gradients: mesic (338-67°), subxeric (68-157° and 248-337°), and xeric (158-247°). Figure 8 The100% minimum convex polygon home ranges for 12 collared 74 Formosan sambar deer in Taroko National Park. The individuals tracked at the same period were shown together at the same partition. Figure 9 Home range overlap proportions among collared Formosan sambar 75 deer individuals. Overlap was computed as individual interactions (see ’Methods’). 謝誌 五年的博士班生涯似乎很短暫，在我越來越發覺自己懂得太少時，學生身份 轉瞬間就結束了，但它又似乎很漫長，成天躲在山裡與動物為伍，令我的人生步 調與身邊的朋友都不太一致，我是很幸運的，這五年來積累的回憶與經歷絕非常 人所能體驗、體會。 父親在我小時候帶我踏進山林，總是鼓勵我”做點跟別人不一樣的事”，希望 我能讓您感到驕傲，也希望在天上的您有看到這一切。非常感謝王穎老師，指導 我獲得獨立思考與獨立研究的能力，在我博士班期間給了我最大程度的自由與支 持，我總是可以自由地安排時間、行程，研究背後又有穩定的經費在支持。謝謝 我的家人們，母親、哥哥總是默默支持我，從不質疑我做想做的事，你們是我最 倚賴的支柱。總是陪在我身邊、最了解我的人是筱筠，謝謝妳的等待與鼓勵，我 很幸運可以與妳結為夫妻。 進出奇萊山區二十餘趟，獲得的幫助實在可以洋洋灑灑寫出一篇直逼論文本 文的謝誌了，因此請恕我以較簡略的方式述說，我的感激難以用文字表達。五年 來的研究經費全靠太魯閣國家公園保育課的研究計劃支持，感謝陳俊山課長熱心 推動水鹿相關研究。捕捉水鹿的過程，衝在最前線拚搏的是一群勇士，包括經驗 豐富的資深布農獵人 Umas、Lingav，教導我們陷阱的架設與捕捉保定方法，一 群志同道合的好友：賴冠榮、陳匡洵、廖昱銓、林子揚、王立豪、張國威、許詩 涵、蔡南益、顏鴻榆、汪仁傑、楊宇帆，以及來自不同部落的布農勇士：高瑩山、 高新興、全志翔、何隼、Abis、鐵馬。捕捉工作中，不可或缺的還有獸醫的加入： 曾美萍、朱何宗、余品奐、毛祈鈞、吳盈慧、官苑芃、陳俊有、吳志純、陳家容、 顏鉅宇、陳儒頎、劉晉嘉、蔡伊婷、吳雯鈴、謝珮瑜，感謝你們暫時拋開工作或 課業跟我來到山上，讓水鹿與研究人員的安全雙雙獲得保障。捕捉的背後還需要 一群人的支援，協助器材準備、記錄、運送物資等，感謝王韋政、林欣怡、高嘉 孜、林祐竹、蔡佩芳、潘玉潔、蔡佳容、林欣儀、何紋靈、韓建國、何鑫、黃致 豪、李昀蒨、高詩豪、林玉佩、鄭淑如、郭正彥、楊琬菁、陳懿文、朱有田、白 欽源、陳佳利、陳添寶等人的熱心參與及幫忙。捕捉後其實才是辛苦的開始，還 有一群人陪著我東奔西跑追蹤水鹿：陳俞佑、程宗德、陸可凡、廖家宏、呂政翰、 莊又澄、黃敏琪、王郁傑、方翔，感謝你們協助這段辛苦的追蹤工作。 我受到的幫助不只在山上，山下還獲得好多其他支援，感謝歐恆佑大力協助 GIS 相關研究技術、林宗以提供水鹿分布資料，謝謝邱惠儀、陳怡君、呂翊維、 林致鋼等實驗室夥伴的後勤支援，感謝郭俊成、祈偉廉兩位獸醫師熱心指導麻醉 相關技術，還有屏科大野生動物收容中心、季昭華老師研究室、吳永惠老師研究 室提供獸醫藥品與器材的支援，在研究初期還受潘明雄及蔡木生指導陷阱架設技 術，及李培芬老師提供研究方法建議，並感謝墾丁國家公園管理處提供捕捉網幫 助研究的進行。 最後謝謝李玲玲老師、裴家騏老師、吳海音老師、謝寶森老師對這個研究的 細心指正，短短兩個小時的口試讓我學到很多，雖然目前還無法修改到盡善盡美， 在未來我仍會繼續努力讓研究更完整而正確。 摘要 欲擬訂有效的野生動物保育與經營管理策略，必須先了解動物的空間使用與 棲地選擇。臺灣水鹿(Rusa unicolor swinhoii)由於遭受到棲息地的破壞以及狩獵的 壓力，被列為保育類野生動物，但目前對其族群狀況及生活史的了解仍十分缺乏， 因此本研究首先以棲地適合度模式(habitat suitability modelling)探討其在臺灣的 分布情形，並找出影響其分布的關鍵因子，接著利用全球定位系統(global positioning system, GPS)追蹤技術，探討水鹿在活動範圍尺度(home-range scale)、 活動範圍內尺度(within-home-range scale)、時間尺度(temporal scale)的棲地選擇， 並了解其空間使用方式。棲地適合度模式顯示水鹿偏好棲息在海拔高於 1,500 m 及遠離公路的地區，臺灣具有 7,865 km2 適合水鹿生存的棲地，這些適合的棲地 主要分布在中央山脈與雪山山脈，但被三條山區省道切割為五個主要區塊，我們 建議監測部分靠近公路的水鹿棲地，未來可嘗試協助其建立亞族群間的交流。此 外，我們於 2009 年 7 月至 2013 年 7 月間藉由 GPS 項圈追蹤了 12 隻水鹿(6 雄 6 雌)，發現水鹿具有季節性移動行為，冷乾季(11 月到 4 月)棲息在海拔較低的地 區(平均 2,483 ± 406 m)，熱濕季(5 月到 10 月)移動到海拔較高的地區(平均 2,984 ± 222 m)。在活動範圍尺度，水鹿偏好使用較平坦的地形及較潮溼的坡向(338-67°)， 在冷乾季偏好使用闊葉林、針闊葉混淆林、開闊地、及鐵杉林，在熱濕季則偏好 冷杉林、箭竹草原、及鐵杉林，整體而言，水鹿能夠廣泛適應各種植被類型。在 活動範圍內尺度，水鹿在冷乾季偏好使用太陽照射時數較高的地點。水鹿的棲地 選擇也發生在時間尺度上，其在日間距森林的距離明顯短於夜間，顯示森林為水 鹿重要的掩蔽處所。在空間使用方面，以最小凸多邊形法(minimum convex polygon)評估水鹿活動範圍大小，發現雄鹿平均年活動範圍為 1,078 ± 501 ha，雌 鹿平均年活動範圍為 1,001 ± 346 ha，活動範圍間的重疊度可高達 80.2%，顯示水 鹿並未建立排他的領域。雄鹿平均日位移 268 ± 90 m，最大日位移 6,435 m，雌 鹿平均日位移 317 ± 135 m，最大日位移 4,422 m。活動範圍與日位移在性別間、 1 季節間都沒有顯著差異。整體而言，我們建立了水鹿棲地選擇與空間使用的完整 資訊，並推測未來水鹿族群擴張的主要限制是人為干擾的相關因子而非自然環境 因子。 關鍵字：地理資訊系統、棲地選擇、活動範圍、水鹿、野生動物經營管理 2 Abstract Studies on the animal space use and habitat selection are required for the conservation and management of large herbivores. In Taiwan, the Formosan sambar deer (Rusa unicolor swinhoii) is listed as a protected species under the wildlife conservation law because of human overexploitation. However, its population status and life history remains unclear. In this study, we used 2 approaches to investigate habitat selection and space use of sambar deer. In the study on geographical-range scale habitat selection, we used habitat suitability modelling to identify key habitat variables and to predict potential distribution of this species throughout Taiwan. In the studies on space use and habitat selections at home-range scale, within-home-range scale, and temporal scale, we tracked the deer by using global positioning system telemetry. The habitat suitability models indicated the presence of 7,865 km2 suitable habitats for the sambar deer in Taiwan. The deer preferentially used areas that were over 1,500 m in elevation and were distant from roads. The results predicted that deer habitats are mainly located in the Central Mountain Range and Xue Mountain Range of Taiwan. However, the predicted habitats were divided into 5 regions, which
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